Pith. sign in

REVIEW 1 cited by

Cross-scale covariance for material property prediction

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2406.05146 v1 pith:ZBAY7UJU submitted 2024-05-29 cond-mat.mtrl-sci

classification cond-mat.mtrl-sci
keywords uncertaintypredictionsimulationsstatisticalstrengthatomscomputedcovariance
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

A simulation can stand its ground against experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale ($\sim 10^8$ atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales ($\leq 10^2$ atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale ``strength-on-predictors'' regression model. This model is then used to quantify uncertainty over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the uncertainty bounds established in our statistical study.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends

    cond-mat.mtrl-sci 2024-11 conditional novelty 6.0 of 10

    A regression model trained on interatomic-potential data predicts the same grain boundary energy scaling from fundamental properties that density functional theory finds, supporting the use of potential ensembles as s...

Pith tools